Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation

被引：818

作者：

He, Wei ^{[1
,2
]}

Dong, Yiting ^{[1
,2
]}

Sun, Changyin ^{[3
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China

[2] Univ Elect Sci & Technol China, Ctr Robot, Chengdu 611731, Peoples R China

[3] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2016年 / 46卷 / 03期

基金：

国家高技术研究发展计划(863计划); 中国国家自然科学基金;

关键词：

Adaptive neural network (NN) control; impedance control; input saturation; learning control; nonlinear system; robot; UNCERTAIN NONLINEAR-SYSTEMS; DISCRETE-TIME-SYSTEMS; TRACKING CONTROL; LEARNING CONTROL; BACKSTEPPING CONTROL; MOBILE MANIPULATORS; OUTPUT CONSTRAINT; NETWORK CONTROL; DELAY SYSTEMS; DESIGN;

D O I：

10.1109/TSMC.2015.2429555

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

In this paper, adaptive impedance control is developed for an n-link robotic manipulator with input saturation by employing neural networks. Both uncertainties and input saturation are considered in the tracking control design. In order to approximate the system uncertainties, we introduce a radial basis function neural network controller, and the input saturation is handled by designing an auxiliary system. By using Lyapunov's method, we design adaptive neural impedance controllers. Both state and output feedbacks are constructed. To verify the proposed control, extensive simulations are conducted.

引用

页码：334 / 344

页数：11

共 60 条

[1]

[Anonymous], 1999, Neural network control of robot manipulators and nonlinear systems

[2]

[Anonymous], 1985, Matrix Analysis

[3]

Behtash S., 1990, INT J CONTROL, V51, P931

[4]

The central nervous system stabilizes unstable dynamics by learning optimal impedance [J].